This work implements a one-dimensional diffusion model that generates a bimodal distribution from noise. We show that the forward process is equivalent to a Langevin equation with a time-dependent coefficient, and that the neural network learns the score function, i.e., the restoring force that enables the diffusion process to be reversed. The model is validated by comparing the theoretical score of the target distribution with that estimated by the network at different time steps, showing excellent agreement, particularly at large times, where it approaches −x. Histograms of the generated samples overlap almost perfectly with the true distribution, demonstrating that the model reproduces the bimodality and captures the reverse Langevin dynamics. This one-dimensional prototype provides a testbed that can be scaled to high-dimensional problems.